Clustering and Regression-Based Analysis of PM2.5 Sensitivity to Meteorology in Cincinnati, Ohio

Abstract

This study identified the meteorological parameters that influence PM2.5 concentrations in the Greater Cincinnati area by employing principal components analysis and multi-variable regression. Meteorological and PM2.5 data were collected over several years to derive statistical relationships about the seasonal variability of meteorological parameters and quantify their influence on PM2.5. We studied the effect of meteorological parameters by seasons and by k-means clustering. The results show that outdoor temperature (OT), planetary boundary height (HPBL) and visibility (VIS) have the strongest effect on PM2.5. The distribution of PM2.5 concentrations in each cluster and season was evaluated using the Kolmogorov–Smirnov test with data fitting using the lognormal and gamma distributions. To our observation, we found the PM2.5 concentration fits the gamma distribution marginally better than the lognormal distribution.